Multi-band radar warning system against drones

Abstract

The application relates to the technical field of unmanned aerial vehicle signal processing, and discloses a multi-band radar early warning system for unmanned aerial vehicle countermeasures, which comprises a multi-band radar detection module, a return data processing module, a target identification and positioning module and an early warning information generation module. The multi-band radar detection module emits radar waves of different frequency bands to obtain return data, the return data processing module calculates a difference degree and a comprehensive difference coefficient for identifying a target, the target identification and positioning module determines the position of an unmanned aerial vehicle, and the early warning information generation module issues an early warning. The system has remarkable beneficial effects, multi-band detection improves accuracy and reliability, accurately identifies and positions a target, can evaluate a threat level, has anti-interference, clutter suppression and environmental self-adaptive capabilities, can distinguish and track multiple targets, effectively solves the shortcomings of existing unmanned aerial vehicle monitoring technologies, provides strong support for unmanned aerial vehicle countermeasures, and guarantees public safety and the stability of key areas.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) signal processing technology, specifically to a multi-band radar early warning system for UAV countermeasures. Background Technology

[0002] In recent years, drones have been widely used in both civilian and commercial fields due to their advantages such as low cost, high flexibility, and ease of operation. In civilian applications, drones are commonly used for aerial photography, agricultural plant protection, and logistics delivery, bringing numerous conveniences to people's lives and work. In the commercial field, they play an important role in power line inspection, surveying and mapping, and advertising, effectively improving the efficiency of various industries.

[0003] However, the widespread use of drones has also raised a series of serious security issues. In critical infrastructure areas, such as airports and military bases, unauthorized drone intrusion can disrupt normal operations and even pose a direct threat to the safety of personnel and equipment. Near airports, a collision between a drone and an aircraft could have catastrophic consequences; around military bases, drones could be used for illegal reconnaissance and theft of sensitive information. In densely populated areas, such as large sporting events and concert venues, uncontrolled drone flights can easily cause injuries or fatalities. Furthermore, drones could be exploited by criminals for smuggling, drug trafficking, and other illegal activities, posing a significant challenge to social security and stability.

[0004] To address the security threats posed by drones, existing drone monitoring and countermeasure technologies are constantly evolving. However, traditional monitoring methods have many limitations. Optical monitoring equipment, such as high-definition cameras, can visually identify the appearance of drones, but they are severely limited by weather conditions. In severe weather, such as heavy rain, dense fog, or sandstorms, their monitoring effectiveness drops significantly or even fails completely; in nighttime environments, insufficient light also makes effective monitoring difficult. Acoustic monitoring technology relies on capturing the noise generated by drones during flight; however, this method is easily affected by ambient noise, has a high false alarm rate in noisy environments such as cities, and cannot accurately obtain the drone's location and trajectory information.

[0005] Radar technology, as a crucial tool for monitoring unmanned aerial vehicles (UAVs), faces numerous challenges in practical applications. When single-band radar detects UAVs, the small size, low radar cross-section (RCS), and constantly changing flight attitude of these drones result in weak and unstable radar echo signals, making them prone to missed detections. Furthermore, in complex electromagnetic environments, single-band radar is susceptible to interference from sources in the same or similar frequency bands, leading to echo signal distortion and reducing the accuracy and reliability of monitoring. For example, in areas with dense communication base stations, radar signals may be severely interfered with by base station signals, rendering them unusable.

[0006] Existing radar early warning systems also fall short of practical requirements in terms of target identification and positioning accuracy. When multiple drones appear in the monitoring area simultaneously, traditional radar early warning systems struggle to quickly and accurately distinguish between different drone targets, and are unable to track their trajectories in real time and with precision, making subsequent countermeasures difficult to implement effectively.

[0007] In terms of echo data processing, existing technologies have limited capabilities when handling radar echo data in complex environments. They cannot fully extract effective information from radar echo data across different frequency bands, making it difficult to accurately extract the characteristics of UAV targets, resulting in low target identification accuracy. Furthermore, existing early warning systems lack environmental adaptability and cannot adjust radar operating parameters in real time according to changes in meteorological conditions (such as wind speed, humidity, and temperature), terrain, and other environmental factors. This leads to significant fluctuations in the system's monitoring performance under different environments, making stable and reliable operation impossible. Summary of the Invention

[0008] The purpose of this invention is to provide a multi-band radar early warning system for countering unmanned aerial vehicles (UAVs) in order to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a multi-band radar early warning system for UAV countermeasures, the system comprising:

[0010] The multi-band radar detection module is used to simultaneously transmit multiple radar waves of different frequencies to scan the monitoring area and obtain radar echo data of the target UAV in different frequency bands.

[0011] The echo data processing module is used to process the echo data acquired by the multi-band radar detection module. The specific processing method is as follows:

[0012] Echo characteristic data of radar waves in different frequency bands under normal conditions are obtained from a local database. For each frequency band, the difference between the echo data and the corresponding normal echo characteristic data is calculated. ,in Indicates the frequency band number. The number of frequency bands used;

[0013] Based on the difference in echo data of each frequency band Calculate the comprehensive difference coefficient , The weighting coefficients for each frequency band are determined through analysis of historical data;

[0014] The target recognition and localization module is used to identify whether there are unmanned aerial vehicle (UAV) targets within the monitoring area and determine their location information based on the results obtained from the echo data processing module; if the comprehensive difference coefficient is considered... If the difference coefficient exceeds a preset threshold, a drone target is identified; the spatial coordinates of the drone are determined by calculating the time delay and phase difference of radar echo signals from different frequency bands. ;

[0015] The early warning information generation module is used to generate early warning information after the target identification and positioning module determines that there is a drone target, and then send the early warning information to the relevant terminal equipment.

[0016] Preferably, the echo data processing module calculates the difference. The specific method used at that time was as follows:

[0017] Perform spectral analysis on the echo data of each frequency band to obtain its spectral characteristics. ;

[0018] spectral characteristics Spectral characteristics of the corresponding frequency band under normal conditions in the local database Compare the two and calculate the Euclidean distance. ;

[0019] Through formula Normalization was performed to obtain the degree of difference. ,in This represents the maximum Euclidean distance between the echo data of this frequency band and the normal spectral characteristics in historical data.

[0020] Preferably, the target recognition and positioning module determines the spatial coordinates of the UAV. The specific method used at that time was as follows:

[0021] Utilizing the difference in arrival time of radar echo signals in different frequency bands According to the speed of radar wave propagation Calculate the distance difference of the drone in different directions. ;

[0022] By combining the distance difference information of radar waves of different frequency bands received from multiple monitoring points, a system of equations is constructed using the triangulation method:

[0023]

[0024] in Here are the coordinates of each monitoring point. To determine the distance from the monitoring point to the reference point, the spatial coordinates of the UAV are obtained by solving this system of equations. .

[0025] Preferably, the target recognition and localization module solves for the UAV's spatial coordinates. When solving the system of equations, an iterative optimization algorithm is used, specifically:

[0026] Using the initially estimated UAV coordinates as initial values, the following objective function is minimized:

[0027]

[0028] Where M represents the number of monitoring points;

[0029] In each iteration, the gradient of the objective function and the Hessian matrix are calculated, and the coordinate values ​​are updated. This continues until the convergence condition is met, including when the change in the objective function is less than a preset threshold.

[0030] To avoid getting stuck in local optima during the iteration process, a random restart strategy is adopted, starting the iteration multiple times from different initial values, and taking the optimal solution as the final UAV spatial coordinates.

[0031] Preferably, the early warning information generated by the early warning information generation module includes a threat level assessment of the UAV target, and the specific assessment method is as follows:

[0032] Retrieve threat level weights corresponding to different flight altitudes, speeds, and approach directions from the local database. ;

[0033] The UAV flight altitude obtained based on the target recognition and positioning module ,speed and flight direction Calculate threat level value ;

[0034] The threat level of the drone is determined based on the preset threat level threshold range.

[0035] Preferably, when selecting the pulse width and repetition frequency of radar waves in different frequency bands, the multi-band radar detection module considers the distribution of interference sources within the monitoring area. Specifically, the method is as follows:

[0036] The frequency range and intensity information of interference sources within the monitoring area are obtained in real time using interference monitoring equipment;

[0037] When the frequency of the interfering source is close to that of a radar wave in a certain frequency band, the selection of pulse width and repetition frequency within the original range of that frequency band is further adjusted to ensure that the pulse width and repetition frequency of the radar wave avoid the parameter range where the interfering source may have an impact. Simultaneously, the transmit power of the radar wave in that frequency band is fine-tuned; the transmit power adjustment formula is as follows:

[0038]

[0039] in The adjusted transmission power, This is the initial transmit power. This is an adjustment factor related to the interference intensity. The strength of the interference source.

[0040] Preferably, the multi-band radar detection module uses phased array technology to achieve beam scanning, specifically as follows:

[0041] An array antenna consists of multiple transmitting / receiving units, each of which independently controls the phase and amplitude of the transmitted signal;

[0042] Based on the target search area and tracking requirements, the phase difference of the signals transmitted by each unit is controlled. This enables beam scanning in different directions, where , The spacing between adjacent units. For beam scanning angle, The wavelength is the radar wave wavelength.

[0043] Preferably, the echo data processing module further includes clutter suppression processing for the echo signal, specifically the following processing method:

[0044] The echo signal is decomposed using a wavelet transform-based method to obtain signals of different frequency sub-bands;

[0045] Based on the energy distribution characteristics of clutter in different sub-bands, a threshold is set to filter the sub-band where the clutter is located, thereby removing the clutter signal.

[0046] The filtered subband signal is reconstructed to obtain the echo signal after clutter suppression.

[0047] Preferably, it also includes an environmental adaptive adjustment module, used to adjust the operating parameters of the multi-band radar detection module in real time according to environmental changes in the monitoring area. The specific adjustment method is as follows:

[0048] Real-time meteorological data, including wind speed, is acquired using meteorological sensors in the monitored area. ,humidity ,temperature ;

[0049] Based on meteorological data, the corresponding radar wave attenuation coefficient is obtained from the local database. ,

[0050] Adjust the radar wave power emitted by the multi-band radar detection module according to the radar wave attenuation coefficient. Adjust the formula to , This is the initial transmit power.

[0051] Preferably, the target recognition and positioning module also has the function of distinguishing and tracking multiple UAV targets, and the specific implementation method is as follows:

[0052] A multi-target tracking algorithm is used to correlate and track the echo data of different UAV targets;

[0053] Based on the motion state information of each drone target, predict its position at the next moment and update the tracking trajectory;

[0054] When new echo data appears, the similarity and correlation probability between the echo data and the existing tracking trajectory are calculated to determine which UAV target the echo data belongs to or whether it is a new target.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] The multi-band radar detection module simultaneously emits radar waves of different frequencies, acquiring echo data of the target UAV from multiple dimensions. Different frequency bands of radar waves interact with UAVs in varying ways, with certain bands being more sensitive to UAVs of specific materials, sizes, or flight attitudes. For example, low-frequency radar waves have strong diffraction capabilities, effectively detecting UAVs hidden behind obstacles; high-frequency radar waves have high resolution, accurately acquiring the UAV's shape and outline information. The data from multiple frequency bands complement each other; even if some bands are interfered with or have weak echo signals, other bands can still provide effective information, significantly reducing the probability of missed detections and enhancing the accuracy and reliability of UAV detection in complex environments.

[0057] The echo data processing module calculates the difference between echo data and normal environmental characteristic data, and combines the weights of each frequency band to derive a comprehensive difference coefficient, enabling accurate identification of UAV targets. It employs methods such as spectrum analysis, Euclidean distance calculation, and normalization processing to effectively extract the unique spectral characteristics of the UAV, distinguishing it from the normal environmental background and reducing false positives. The target recognition and localization module utilizes the time delay and phase difference of radar wave echo signals from different frequency bands, combined with triangulation to construct a system of equations to determine the UAV's spatial coordinates. It uses an iterative optimization algorithm to solve the problem and employs a random restart strategy to avoid local optima, significantly improving positioning accuracy and providing precise target location information for subsequent countermeasures.

[0058] The early warning information generation module calculates a threat level value based on the drone's flight altitude, speed, and approach direction, combined with threat level weights in the local database, and determines the threat level according to a preset threshold range. This allows relevant personnel to quickly understand the drone's threat level and take targeted measures. For high-threat drones, emergency countermeasures are activated; for low-threat drones, relatively mild regulatory measures are adopted to improve response efficiency and rationally allocate security resources.

[0059] When selecting pulse width and repetition frequency, the multi-band radar detection module takes into account the distribution of interference sources in the monitoring area and adjusts accordingly to avoid interference parameter ranges. It also fine-tunes the transmit power based on the interference intensity. Phased array technology is used to achieve beam scanning. By precisely controlling the phase and amplitude of the transmitted signals from each element, the radar beam can be flexibly pointed towards the target, enhancing the detection capability of specific areas while reducing the impact of interference from other directions. These measures effectively reduce the impact of interference on the radar signal, ensuring stable operation in complex electromagnetic environments.

[0060] The echo data processing module employs a wavelet transform-based clutter suppression method to decompose the echo signal, filter it according to the clutter energy distribution characteristics, and reconstruct the signal. This effectively removes interference from ground clutter, meteorological clutter, and other sources, improving the signal-to-noise ratio of the echo data, providing more accurate data for target identification and positioning, and enhancing the overall system performance.

[0061] The environmental adaptive adjustment module utilizes meteorological sensors to acquire real-time meteorological data, retrieves the corresponding radar wave attenuation coefficient from a local database, and adjusts the radar wave transmission power. This ensures that the radar maintains stable detection performance under different meteorological conditions, unaffected by factors such as wind speed, humidity, and temperature. In windy weather, it increases transmission power to compensate for radar wave propagation attenuation, maintain detection range and accuracy, and improve the system's environmental adaptability and stability.

[0062] The target recognition and localization module employs a multi-target tracking algorithm to correlate and track echo data from different drones. It predicts the next location based on the drone's motion status information and updates the tracking trajectory. When new echo data appears, it determines whether it belongs to a new target by calculating similarity and association probability. In complex scenarios where multiple drones appear simultaneously, it accurately distinguishes and tracks each drone, monitoring its movement trajectory in real time, providing strong support for comprehensive monitoring and effective countermeasures. Attached Figure Description

[0063] Figure 1 This is a schematic diagram illustrating the working principle of the multi-band radar early warning system described in this invention.

[0064] Figure 2 A diagram illustrating the steps involved in determining the UAV's spatial coordinates for the target recognition and localization module;

[0065] Figure 3 This diagram illustrates the implementation steps of a multi-band radar detection module to handle interference and beam scanning. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Please see Figure 1-3 This invention provides a technical solution: a multi-band radar early warning system for countering unmanned aerial vehicles (UAVs), the system comprising:

[0068] Multi-band radar detection module: By simultaneously emitting radar waves of multiple different frequency bands, it comprehensively scans the monitored area. In practical applications, such as important event venues and sensitive areas, radar waves of different frequency bands can acquire information about the target drone from multiple dimensions, improving the accuracy and reliability of detection. These radar waves generate echoes upon encountering the target drone. The multi-band radar detection module acquires these radar echo data at different frequency bands and transmits them to subsequent modules for processing.

[0069] Echo data processing module: After receiving echo data from the multi-band radar detection module, it first retrieves echo characteristic data of radar waves of different frequency bands under normal conditions from the local database. The local database pre-stores a large amount of normal echo characteristic data obtained through actual measurement and analysis, covering the normal performance of radar waves of different frequency bands under various environmental conditions. For the echo data of each frequency band, it calculates the degree of difference between its echo data and the corresponding normal echo characteristic data. ( Indicates the frequency band number. (This refers to the number of frequency bands used). By analyzing these differences, it is possible to determine whether there are any anomalies in the monitoring area, and thus identify drone targets.

[0070] Based on the difference in echo data of each frequency band Calculate the comprehensive difference coefficient The calculation formula is: ,in These are the weighting coefficients for each frequency band. Through in-depth analysis of a large amount of historical data, it was determined that different frequency bands have different importance in identifying drone targets. For example, some frequency bands may be more sensitive to certain types of drones. By reasonably setting the weighting coefficients, the actual situation of the monitoring area can be reflected more accurately.

[0071] Target recognition and localization module: Based on the comprehensive difference coefficient obtained from the echo data processing module. To identify whether there are drone targets within the monitoring area. If the overall difference coefficient is considered... If the difference coefficient exceeds the preset threshold, it indicates a significant difference from the normal environment within the monitored area, thus confirming the presence of a drone target. Then, by precisely calculating the time delay and phase difference of radar echo signals from different frequency bands, the spatial coordinates of the drone are determined. In practical applications, this process involves complex signal processing and mathematical calculations, which can provide accurate location information of the drone for subsequent countermeasures.

[0072] Early Warning Information Generation Module: This module activates after the target identification and positioning module determines the presence of a drone target. It generates detailed early warning information and promptly sends this information to relevant terminal devices, such as handheld terminals for security personnel and displays in the monitoring center. This early warning information enables relevant personnel to react quickly and take appropriate countermeasures to ensure the safety of the monitored area.

[0073] The present invention will be further described below with reference to Examples 1 to 5:

[0074] Example 1:

[0075] This embodiment mainly describes how the echo data processing module calculates the difference. The specific method aims to more accurately quantify the difference between echo data and normal echo characteristic data for each frequency band.

[0076] In practice, the echo data processing module performs spectral analysis on the echo data for each frequency band. Spectral analysis is a commonly used signal processing technique that uses methods such as Fourier transform to convert time-domain echo data to the frequency domain and obtain its spectral characteristics. For example, suppose there are currently three frequency bands. When performing spectral analysis on the echo data of frequency band 1, the Fast Fourier Transform (FFT) algorithm is used to convert the acquired time-series echo data into spectral data with frequency distribution, thereby obtaining the spectral characteristics of frequency band 1. .

[0077] Obtain spectral features Then, it was compared with the spectral characteristics of the corresponding frequency band under normal conditions in the local database. A comparison is made. This embodiment uses the calculation of Euclidean distance. The Euclidean distance is a mathematically intuitive way to measure the degree of difference between two vectors. For example, if spectral features are represented by vectors, for frequency band 2, the spectral feature vector of its current echo data is... The spectral feature vector under normal conditions is Then the Euclidean distance between them .

[0078] To ensure comparability of the differences calculated across different frequency bands, the Euclidean distance needs to be normalized. This is achieved through the formula... Normalization is performed, where This represents the maximum Euclidean distance between the echo data and the normal spectral characteristics of this frequency band in historical data. For example, for frequency band 3, the maximum Euclidean distance between its echo data and the normal spectral characteristics in historical data is... The currently calculated Euclidean distance is The difference in frequency band 3 After this processing, the degree of difference... The value ranges from 0 to 1, which facilitates the subsequent calculation of the comprehensive difference coefficient and target identification.

[0079] Example 2:

[0080] This embodiment details how the target recognition and localization module determines the spatial coordinates of the UAV. The specific process involves accurately obtaining the drone's position in space after identifying the drone target, providing crucial location information for countermeasures.

[0081] Utilizing the difference in arrival time of radar echo signals in different frequency bands According to the speed of radar wave propagation Calculate the distance difference of the drone in different directions. In practical systems, multi-band radar detection modules record the precise times of radar wave transmission and reception echoes at different frequency bands, and calculate the time difference to obtain... For example, suppose the radar wave transmission time of frequency band 1 is... The echo reception time is The radar wave transmission time of frequency band 2 is The echo reception time is , , Then the distance difference is calculated. , .

[0082] By combining the distance difference information of radar waves in different frequency bands received from multiple monitoring points, a system of equations is constructed using triangulation. Assume there are... The coordinates of each monitoring point are: The distance from the monitoring point to the reference point is The system of equations constructed is as follows:

[0083]

[0084] In solving this system of equations, the target recognition and localization module employs an iterative optimization algorithm. Using the initially estimated UAV coordinates as initial values, the objective function is minimized. The objective function is:

[0085]

[0086] in This represents the number of monitoring points.

[0087] In each iteration, the gradient and Hessian matrix of the objective function are calculated. The gradient reflects the rate of change of the objective function at the current point, while the Hessian matrix describes the curvature of the objective function. Using this information, optimization algorithms (such as Newton's method, quasi-Newton methods, etc.) are employed to update the coordinate values. For example, when using Newton's method, the coordinate update formula is: ,in The coordinates for the current iteration. For Hessian matrix, The gradient is used to iterate until the convergence condition is met, that is, the change in the objective function is less than the preset threshold.

[0088] To avoid getting stuck in local optima during the iteration process, a random restart strategy is adopted. Multiple iterations are performed starting from different initial values, each iteration is conducted independently, and the solution obtained in each iteration is recorded. Finally, the optimal solution is selected as the final UAV spatial coordinates. For example, 10 random restart iterations are performed, each with a different initial coordinate value. The objective function values ​​obtained in these 10 iterations are compared, and the solution with the smallest objective function value is selected as the final UAV spatial coordinates.

[0089] Example 3:

[0090] This embodiment describes the specific method for assessing the threat level of drone targets in the early warning information generated by the early warning information generation module. Its purpose is to enable relevant personnel to take different levels of countermeasures according to the threat level of the drone, thereby improving response efficiency and security.

[0091] The early warning information generation module obtains threat level weights corresponding to different flight altitudes, speeds, and approach directions from the local database. , , These weights, stored in the local database, were derived from the analysis of extensive historical data and the threat levels of drones in different scenarios. For example, near airports, drones pose a greater threat to aircraft takeoff and landing safety when flying at lower altitudes; therefore, weights are assigned to lower flight altitudes. The speed is relatively high, and in military-sensitive areas, the threat is even greater when drones approach at high speeds; therefore, the corresponding speed weighting is higher. The settings are set too high.

[0092] The UAV flight altitude obtained based on the target recognition and positioning module ,speed and flight direction Calculate threat level value The calculation formula is: Suppose that in a certain monitoring scenario, the flight altitude of a drone is obtained. meters, speed meters per second, flight direction (Assuming the reference is the direction perpendicular to the boundary of the monitoring area, and the included angle is...) The weight (converted to a numerical representation using trigonometric functions) is 0.5, obtained from the local database. The threat level value .

[0093] The threat level of the drone is determined based on a preset threat level threshold range. For example, the preset threat level threshold range is: low threat level. Medium threat level High threat level Based on the above calculations... If the threat level is not specified, the drone is classified as a medium threat. When generating a warning message, the drone's threat level will be clearly indicated to allow relevant personnel to make quick decisions.

[0094] Example 4:

[0095] This embodiment mainly describes the method of multi-band radar detection module to deal with interference sources when selecting the pulse width and repetition frequency of radar waves in different frequency bands, and the specific method of using phased array technology to achieve beam scanning. Its function is to improve the anti-interference capability of radar detection and ensure that UAV targets in the monitoring area can be effectively detected.

[0096] Multi-band radar detection modules utilize interference monitoring equipment to acquire real-time information on the frequency range and intensity of interference sources within the monitored area. This interference monitoring equipment can be a specialized spectrum analyzer capable of continuously monitoring electromagnetic signals in the surrounding environment and identifying the frequency and intensity of interference sources. For example, the interference monitoring equipment might detect a frequency range within the monitored area... A strong source of interference.

[0097] When the frequency of an interfering source is close to that of a radar wave in a certain frequency band, the selection of pulse width and repetition frequency within the original range of that frequency band is further adjusted so that the pulse width and repetition frequency of the radar wave avoid the parameter range where the interfering source may have an impact. For example, if the original pulse width selection range of a radar wave in a certain frequency band is... The range of repetition frequency selection is The interference source frequency is close to this frequency band, and analysis shows that the pulse width range that the interference may affect is... The repetition frequency range is Therefore, when selecting radar wave parameters for this frequency band, avoid these ranges and choose options such as pulse widths of [missing information]. The repetition frequency is .

[0098] Simultaneously, fine-tuning of the radar wave transmission power in this frequency band is added; the transmission power adjustment formula is as follows: ,in The adjusted transmission power, This is the initial transmit power. This is an adjustment factor related to the interference intensity. Let's consider the interference source strength. Assume the initial transmitted power of a radar wave in a certain frequency band. Interference source strength (Unit determined based on actual measurement), adjustment coefficient The adjusted transmit power .

[0099] The multi-band radar detection module employs phased array technology to achieve beam scanning. It consists of an array antenna composed of multiple transmit / receive units, each independently controlling the phase and amplitude of its transmitted signal. The phase difference of the transmitted signals from each unit is controlled according to the target search area and tracking requirements. This enables beam scanning in different directions; the phase difference calculation formula is as follows: ,in The spacing between adjacent units. For beam scanning angle, This refers to the radar wave wavelength. For example, the spacing between adjacent elements in an array antenna. radar wave wavelength To adjust the beam scanning angle Then the phase difference By precisely controlling the phase difference of each unit, it is possible to detect and track targets in different directions.

[0100] Example 5:

[0101] This embodiment mainly describes the clutter suppression processing method of the echo data processing module for echo signals, the working mode of the environment adaptive adjustment module in the system, and the function of the target recognition and positioning module to distinguish and track multiple UAV targets. Its purpose is to improve the quality of echo data, enable the system to adapt to different environmental changes, and accurately distinguish and track multiple UAV targets.

[0102] The echo data processing module employs a wavelet transform-based method to suppress clutter in the echo signal. First, the echo signal is decomposed using wavelet transform to obtain signals in different frequency sub-bands. Wavelet transform is a time-frequency analysis method that can decompose a signal into different frequency sub-bands. For example, the Daubechies wavelet can be used to decompose the echo signal into a low-frequency sub-band and multiple high-frequency sub-band signals.

[0103] Based on the energy distribution characteristics of clutter in different sub-bands, thresholds are set to filter the sub-bands containing clutter and remove clutter signals. Through analysis of a large amount of historical echo data, it is determined that clutter is mainly concentrated in certain high-frequency sub-bands, such as the 3rd and 4th high-frequency sub-bands with higher energy. Appropriate thresholds are set; for example, in the 3rd sub-band, signals with an amplitude greater than 5 (determined according to the actual situation) are considered clutter, and these signals with amplitudes greater than the threshold are filtered.

[0104] The filtered sub-band signals are reconstructed to obtain clutter-suppressed echo signals. Inverse wavelet transform is then used to recombine the filtered sub-band signals, recovering the clutter-suppressed echo signals and improving the quality of the echo data, thus providing more accurate data for subsequent target identification and localization.

[0105] The environmental adaptive adjustment module adjusts the operating parameters of the multi-band radar detection module in real time according to environmental changes in the monitored area. Real-time meteorological data, including wind speed, is acquired using meteorological sensors in the monitored area. ,humidity ,temperature Meteorological sensors can be a combination of various types of sensors, such as an anemometer to measure wind speed, a humidity sensor to measure humidity, and a temperature sensor to measure temperature.

[0106] Based on meteorological data, the corresponding radar wave attenuation coefficient is obtained from the local database. , , The local database pre-stores radar wave attenuation coefficients under different meteorological conditions; these coefficients are obtained through actual measurements and theoretical calculations. For example, when wind speed... ,humidity ,temperature At that time, retrieved from the database .

[0107] Adjust the radar wave power emitted by the multi-band radar detection module according to the radar wave attenuation coefficient. Adjust the formula to , This is the initial transmit power. Assume the initial transmit power... Based on the attenuation coefficient obtained above, the adjusted transmission power By adjusting the radar wave transmission power in real time, the radar can stably and effectively detect target drones under different weather conditions.

[0108] The target recognition and localization module also has the ability to distinguish and track multiple UAV targets. It employs multi-target eye-tracking algorithms, such as the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), the Probabilistic Data Association Filter (PDAF), and the Multiple Hypothesis Tracking (MHT) algorithm. Taking the Kalman Filter algorithm as an example, it is based on a state-space model and estimates the state (such as position and velocity) of each UAV target through two steps: prediction and update.

[0109] In practical applications, the next position of each UAV target is predicted based on its motion state information, and the tracking trajectory is updated accordingly. Assume the current position coordinates of a UAV are... The speed is Based on kinematic formulas, predict the position at the next moment. ,in The time interval is defined as . Kalman filtering continuously predicts and updates, utilizing information from the echo data to gradually correct the predicted values, thereby more accurately tracking the drone's trajectory.

[0110] When new echo data appears, its similarity and association probability with existing tracking trajectories are calculated to determine which UAV target the echo data belongs to or whether it is a new target. Similarity can be calculated using various methods, such as Mahalanobis distance, which calculates the distance between the new echo data and the predicted position of the existing eye-tracking trajectory; the smaller the distance, the higher the similarity. Association probability can be calculated using probabilistic data association algorithms, comprehensively considering the degree of matching between the new echo data and each tracking trajectory to determine its affiliation. For example, after calculation, the Mahalanobis distance between the new echo data and UAV target A is... The Mahalanobis distance between the drone target B and the target B is... ,like Furthermore, based on the correlation probability calculation, if the correlation probability with target A is greater than a set threshold (e.g., 0.7), then the new echo data is determined to belong to UAV target A; if the correlation probability with all existing targets is less than the threshold, then it is determined to be a new target, and a new eye-tracking trajectory establishment process is initiated. In this way, the target recognition and positioning module can accurately distinguish and track multiple UAV targets in complex environments, providing reliable target information for subsequent early warning and countermeasures.

[0111] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0112] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-band radar early warning system for countering unmanned aerial vehicles (UAVs), characterized in that: include: The multi-band radar detection module is used to simultaneously transmit multiple radar waves of different frequencies to scan the monitoring area and obtain radar echo data of the target UAV in different frequency bands. The echo data processing module is used to process the echo data acquired by the multi-band radar detection module. The specific processing method is as follows: Echo characteristic data of radar waves in different frequency bands under normal conditions are obtained from a local database. For each frequency band, the difference between the echo data and the corresponding normal echo characteristic data is calculated. ,in Indicates the frequency band number. The number of frequency bands used; Based on the difference in echo data of each frequency band Calculate the overall difference coefficient , The weighting coefficients for each frequency band are determined through analysis of historical data; The target recognition and localization module is used to identify whether there are UAV targets in the monitoring area and determine their location information based on the results obtained from the echo data processing module. If the comprehensive difference coefficient If the difference coefficient exceeds a preset threshold, a drone target is identified; the spatial coordinates of the drone are determined by calculating the time delay and phase difference of radar echo signals from different frequency bands. ; The early warning information generation module is used to generate early warning information after the target identification and positioning module determines that there is a drone target, and send the early warning information to relevant terminal devices; The echo data processing module calculates the difference. The specific method used at that time was as follows: Perform spectral analysis on the echo data of each frequency band to obtain its spectral characteristics. ; spectral characteristics Spectral characteristics of the corresponding frequency band under normal conditions in the local database Compare the two and calculate the Euclidean distance. ; Through formula Normalization was performed to obtain the degree of difference. ,in This represents the maximum Euclidean distance between the echo data of this frequency band and the normal spectral characteristics in historical data.

2. The multi-band radar early warning system for UAV countermeasures according to claim 1, characterized in that, The target recognition and localization module determines the spatial coordinates of the UAV. The specific method used at that time was as follows: Utilizing the difference in arrival time of radar echo signals in different frequency bands According to the speed of radar wave propagation Calculate the distance difference of the drone in different directions. ; By combining the distance difference information of radar waves of different frequency bands received from multiple monitoring points, a system of equations is constructed using the triangulation method: ； in Here are the coordinates of each monitoring point. To determine the distance from the monitoring point to the reference point, the spatial coordinates of the UAV are obtained by solving this system of equations. .

3. The multi-band radar early warning system for UAV countermeasures according to claim 2, characterized in that, The target recognition and localization module solves the UAV's spatial coordinates. When solving the system of equations, an iterative optimization algorithm is used, specifically: Using the initially estimated UAV coordinates as initial values, the following objective function is minimized: Where M is the number of monitoring points; In each iteration, the gradient of the objective function and the Hessian matrix are calculated, and the coordinate values ​​are updated. This continues until the convergence condition is met, including when the change in the objective function is less than a preset threshold. To avoid getting stuck in local optima during the iteration process, a random restart strategy is adopted, starting the iteration multiple times from different initial values, and taking the optimal solution as the final UAV spatial coordinates.

4. The multi-band radar early warning system for UAV countermeasures according to claim 1, characterized in that, The early warning information generated by the early warning information generation module includes a threat level assessment of the drone target, and the specific assessment method is as follows: Retrieve threat level weights corresponding to different flight altitudes, speeds, and approach directions from the local database. ; The UAV flight altitude obtained based on the target recognition and positioning module ,speed and flight direction Calculate threat level value ; The threat level of the drone is determined based on the preset threat level threshold range.

5. The multi-band radar early warning system for UAV countermeasures according to claim 1, characterized in that, When selecting the pulse width and repetition frequency of radar waves in different frequency bands, the multi-band radar detection module considers the distribution of interference sources within the monitoring area. The specific method is as follows: The frequency range and intensity information of interference sources within the monitoring area are obtained in real time using interference monitoring equipment; When the frequency of the interfering source is close to that of a radar wave in a certain frequency band, the selection of pulse width and repetition frequency within the original range of that frequency band is further adjusted to ensure that the pulse width and repetition frequency of the radar wave avoid the parameter range where the interfering source may have an impact. Simultaneously, the transmit power of the radar wave in that frequency band is fine-tuned; the transmit power adjustment formula is as follows: ; in The adjusted transmission power, This is the initial transmit power. This is an adjustment factor related to the interference intensity. The strength of the interference source.

6. The multi-band radar early warning system for UAV countermeasures according to claim 5, characterized in that, The multi-band radar detection module uses phased array technology to achieve beam scanning, specifically as follows: An array antenna consists of multiple transmitting / receiving units, each of which independently controls the phase and amplitude of the transmitted signal; Based on the target search area and tracking requirements, the phase difference of the signals transmitted by each unit is controlled. This enables beam scanning in different directions, where , The spacing between adjacent units. For beam scanning angle, The wavelength is the radar wave wavelength.

7. The multi-band radar early warning system for UAV countermeasures according to claim 1, characterized in that, The echo data processing module also includes clutter suppression processing for the echo signal, specifically as follows: The echo signal is decomposed using a wavelet transform-based method to obtain signals in different frequency sub-bands; based on the energy distribution characteristics of clutter in different sub-bands, a threshold is set to filter the sub-band containing clutter and remove the clutter signal. The filtered subband signal is reconstructed to obtain the echo signal after clutter suppression.

8. The multi-band radar early warning system for UAV countermeasures according to claim 1, characterized in that, It also includes an environment adaptive adjustment module, which is used to adjust the operating parameters of the multi-band radar detection module in real time according to changes in the environment of the monitoring area. The specific adjustment method is as follows: Real-time meteorological data, including wind speed, is acquired using meteorological sensors in the monitored area. ,humidity ,temperature Based on meteorological data, the corresponding radar wave attenuation coefficient is obtained from the local database. , Adjust the radar wave power emitted by the multi-band radar detection module according to the radar wave attenuation coefficient. Adjust the formula to , This is the initial transmit power.

9. The multi-band radar early warning system for UAV countermeasures according to claim 1, characterized in that, The target recognition and localization module also has the function of distinguishing and tracking multiple UAV targets, and the specific implementation method is as follows: A multi-target tracking algorithm is used to correlate and track the echo data of different UAV targets; Based on the motion state information of each drone target, predict its position at the next moment and update the tracking trajectory; When new echo data appears, the similarity and correlation probability between the echo data and the existing tracking trajectory are calculated to determine which UAV target the echo data belongs to or whether it is a new target.

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